code: def test(self):
      case1 = self.exp.predict_surrogate(
          new_observation=self.X.iloc[1, :],
          feature_names=self.X.columns,
      )
      case2 = self.exp.predict_surrogate(
          new_observation=self.X.iloc[1:2, :],
          mode="classification",
          feature_names=self.X.columns,
          discretize_continuous=True,
          num_features=4,
      )
      case3 = self.exp.predict_surrogate(
          new_observation=self.X.iloc[1:2, :].to_numpy(),
          feature_names=self.X.columns,
          kernel_width=2,
          num_samples=50,
      )
      case4 = self.exp2.predict_surrogate(
          new_observation=self.X2.iloc[1, :],
          feature_names=self.X2.columns,
      )
      case5 = self.exp2.predict_surrogate(
          new_observation=self.X2.iloc[1:2, :],
          mode="regression",
          feature_names=self.X2.columns,
          discretize_continuous=True,
          num_features=4,
      )
      case6 = self.exp2.predict_surrogate(
          new_observation=self.X2.iloc[1:2, :].to_numpy(),
          feature_names=self.X2.columns,
          kernel_width=2,
          num_samples=50,
      )
  
      self.assertIsInstance(case1, lime.explanation.Explanation)
      self.assertIsInstance(case2, lime.explanation.Explanation)
      self.assertIsInstance(case3, lime.explanation.Explanation)
      self.assertIsInstance(case4, lime.explanation.Explanation)
      self.assertIsInstance(case5, lime.explanation.Explanation)
      self.assertIsInstance(case6, lime.explanation.Explanation)
  
file_path: DALEX/python/dalex/test/test_predict_surrogate.py
imports: import unittest
  from sklearn.neural_network import MLPClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.preprocessing import LabelEncoder
  import dalex
  import lime
label_desc: Use image node_id 2 for calling the PredictSurrogateTestTitanic obj's underlying member method code with example usage: obj.test() without return types